Academic Journal
T1-weighting in Steady-State FLASH MRI-Diffusion Is Not Only Supportive but Mandatory for the Contrast.
| Title: | T1-weighting in Steady-State FLASH MRI-Diffusion Is Not Only Supportive but Mandatory for the Contrast. |
|---|---|
| Authors: | Weinmüller S; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany., Chellapandian DC; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.; Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany., Endres J; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany., Leupold J; Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany., Gritsch F; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany., Wagner F; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany., Schneider R; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany., Zaiss M; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.; Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. |
| Source: | Magnetic resonance in medicine [Magn Reson Med] 2026 Oct; Vol. 96 (4), pp. 1789-1797. Date of Electronic Publication: 2026 Jun 21. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1999- : New York, NY : Wiley Original Publication: San Diego : Academic Press |
| MeSH Terms: | Brain*/diagnostic imaging , Diffusion Magnetic Resonance Imaging*/methods , Image Processing, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods, Silicone Oils/chemistry ; Cerebrospinal Fluid/diagnostic imaging ; Phantoms, Imaging ; Computer Simulation ; Humans ; Algorithms ; Diffusion ; Printing, Three-Dimensional |
| Abstract: | Purpose: FLASH imaging is widely assumed to produce a T Methods: FLASH sequences were simulated using phase graph simulations using a synthetic brain phantom with and without diffusion and realistic PD values to show the contrast change. The impact of neglecting diffusion in synthetic training data was evaluated using a segmentation network trained on simulated data and tested on in vivo measurement. Experimental validation of the contrast change was performed using a 3D-printed brain phantom using silicone oil as a low-diffusivity compartment. Results: Without diffusion, simulations showed CSF signal intensities higher than WM, resulting in a contrast change. Diffusion suppresses higher-order echoes in long T Conclusion: Diffusion is essential for realistic FLASH simulations of long T (© 2026 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.) |
| References: | M. Markl and J. Leupold, “Gradient Echo Imaging,” Journal of Magnetic Resonance Imaging 35, no. 6 (2012): 1274–1289, https://doi.org/10.1002/jmri.23638. A. Haase, J. Frahm, D. Matthaei, W. Hanicke, and K.‐D. Merboldt, “FLASH Imaging. Rapid NMR Imaging Using Low Flip‐Angle Pulses,” Journal of Magnetic Resonance (1969) 67, no. 2 (1986): 258–266, https://doi.org/10.1016/0022‐2364(86)90433‐6. A. P. Crawley, M. L. Wood, and R. M. Henkelman, “Elimination of Transverse Coherences in FLASH MRI,” Magnetic Resonance in Medicine 8, no. 3 (1988): 248–260, https://doi.org/10.1002/mrm.1910080303. Y. Zur, M. L. Wood, and L. J. Neuringer, “Spoiling of Transverse Magnetization in Steady‐State Sequences,” Magnetic Resonance in Medicine 21, no. 2 (1991): 251–263, https://doi.org/10.1002/mrm.1910210210. W. T. Sobol and D. M. Gauntt, “On the Stationary States in Gradient Echo Imaging,” Journal of Magnetic Resonance Imaging 6, no. 2 (1996): 384–398, https://doi.org/10.1002/jmri.1880060220. J. Leupold, M. Weigel, and S. Bär, “On the Choice of the Phase Difference Increment in Radiofrequency‐Spoiled Gradient‐Echo Magnetic Resonance Imaging of Liquids With Consideration of Diffusion,” PLoS One 20, no. 5 (2025): e0324455, https://doi.org/10.1371/journal.pone.0324455. C. Preibisch and R. Deichmann, “Influence of RF Spoiling on the Stability and Accuracy of T1 Mapping Based on Spoiled FLASH With Varying Flip Angles,” Magnetic Resonance in Medicine 61, no. 1 (2009): 125–135, https://doi.org/10.1002/mrm.21776. V. Denolin, C. Azizieh, and T. Metens, “New Insights Into the Mechanisms of Signal Formation in RF‐Spoiled Gradient Echo Sequences,” Magnetic Resonance in Medicine 54, no. 4 (2005): 937–954, https://doi.org/10.1002/mrm.20652. R. F. Busse and S. J. Riederer, “Steady‐State Preparation for Spoiled Gradient Echo Imaging,” Magnetic Resonance in Medicine 45, no. 4 (2001): 653–661, https://doi.org/10.1002/mrm.1088. J. H. Duyn, “Steady State Effects in Fast Gradient Echo Magnetic Resonance Imaging,” Magnetic Resonance in Medicine 37, no. 4 (1997): 559–568, https://doi.org/10.1002/mrm.1910370414. K. Nehrke, “On the Steady‐State Properties of Actual Flip Angle Imaging (AFI),” Magnetic Resonance in Medicine 61, no. 1 (2009): 84–92, https://doi.org/10.1002/mrm.21592. N. Corbin and M. F. Callaghan, “Imperfect Spoiling in Variable Flip Angle T1 Mapping at 7T: Quantifying and Minimizing Impact,” Magnetic Resonance in Medicine 86, no. 2 (2021): 693–708, https://doi.org/10.1002/mrm.28720. R. Treier, A. Steingoetter, M. Fried, W. Schwizer, and P. Boesiger, “Optimized and Combined T1 and B1 Mapping Technique for Fast and Accurate T1 Quantification in Contrast‐Enhanced Abdominal MRI,” Magnetic Resonance in Medicine 57, no. 3 (2007): 568–576, https://doi.org/10.1002/mrm.21177. V. L. Yarnykh, “Optimal Radiofrequency and Gradient Spoiling for Improved Accuracy of T1 and B1 Measurements Using Fast Steady‐State Techniques,” Magnetic Resonance in Medicine 63, no. 6 (2010): 1610–1626, https://doi.org/10.1002/mrm.22394. F. H. Epstein, J. P. Mugler, and J. R. Brookeman, “Spoiling of Transverse Magnetization in Gradient‐Echo (GRE) Imaging During the Approach to Steady State,” Magnetic Resonance in Medicine 35, no. 2 (1996): 237–245, https://doi.org/10.1002/mrm.1910350216. S. Weinmüller, J. Endres, H. N. Dang, R. Stollberger, and M. Zaiss, “MR‐Zero Meets FLASH—Controlling the Transient Signal Decay in Gradient‐ and Rf‐Spoiled Gradient Echo Sequences,” Magnetic Resonance in Medicine 93 (2024): 942–960, https://doi.org/10.1002/mrm.30318. C. Ganter, “Steady State of Echo–Shifted Sequences With Radiofrequency Phase Cycling,” Magnetic Resonance in Medicine 56, no. 4 (2006): 923–926, https://doi.org/10.1002/mrm.21010. R. Heule, C. Ganter, and O. Bieri, “Variable Flip Angle T1 Mapping in the Human Brain With Reduced T2 Sensitivity Using Fast Radiofrequency‐Spoiled Gradient Echo Imaging,” Magnetic Resonance in Medicine 75, no. 4 (2016): 1413–1422, https://doi.org/10.1002/mrm.25668. K. E. Keenan, M. Ainslie, A. J. Barker, et al., “Quantitative Magnetic Resonance Imaging Phantoms: A Review and the Need for a System Phantom,” Magnetic Resonance in Medicine 79, no. 1 (2018): 48–61, https://doi.org/10.1002/mrm.26982. M. E. Komlosh, D. Benjamini, A. S. Barnett, et al., “Anisotropic Phantom to Calibrate High‐q Diffusion MRI Methods,” Journal of Magnetic Resonance 275 (2017): 19–28, https://doi.org/10.1016/j.jmr.2016.11.017. C. Poupon, B. Rieul, I. Kezele, M. Perrin, F. Poupon, and J. Mangin, “New Diffusion Phantoms Dedicated to the Study and Validation of High‐Angular‐Resolution Diffusion Imaging (HARDI) Models,” Magnetic Resonance in Medicine 60, no. 6 (2008): 1276–1283, https://doi.org/10.1002/mrm.21789. H. M. Gach, “Technical Note: T1 and T2 and Complex Permittivities of Mineral Oil, Silicone Oil, and Glycerol at 0.35, 1.5, and 3 T,” Medical Physics 46, no. 4 (2019): 1785–1792, https://doi.org/10.1002/mp.13429. B. Webb, “Temperature Dependence of Viscosity, Relaxation Times (T1, T2) and Simulated Contrast for Potential Perfusates in Post‐Mortem MR Angiography (PMMRA),” International Journal of Legal Medicine 131, no. 3 (2017): 739–749, https://doi.org/10.1007/s00414‐016‐1482‐5. N. Scholand, X. Wang, V. Roeloffs, S. Rosenzweig, and M. Uecker, “Quantitative MRI by Nonlinear Inversion of the Bloch Equations,” Magnetic Resonance in Medicine 90, no. 2 (2023): 520–538, https://doi.org/10.1002/mrm.29664. J. Fessler, “Model‐Based Image Reconstruction for MRI,” IEEE Signal Processing Magazine 27, no. 4 (2010): 81–89, https://doi.org/10.1109/MSP.2010.936726. A. S. Lundervold and A. Lundervold, “An Overview of Deep Learning in Medical Imaging Focusing on MRI,” Zeitschrift für Medizinische Physik 29, no. 2 (2019): 102–127, https://doi.org/10.1016/j.zemedi.2018.11.002. C. G. Xanthis, D. Filos, K. Haris, and A. H. Aletras, “Simulator‐Generated Training Datasets as an Alternative to Using Patient Data for Machine Learning: An Example in Myocardial Segmentation With MRI,” Computer Methods and Programs in Biomedicine 198 (2021): 105817, https://doi.org/10.1016/j.cmpb.2020.105817. Y. Chen, Z. Fang, S.‐C. Hung, W.‐T. Chang, D. Shen, and W. Lin, “High‐Resolution 3D MR Fingerprinting Using Parallel Imaging and Deep Learning,” NeuroImage 206 (2020): 116329, https://doi.org/10.1016/j.neuroimage.2019.116329. D. Ma, V. Gulani, N. Seiberlich, et al., “Magnetic Resonance Fingerprinting,” Nature 495, no. 7440 (2013): 187–192, https://doi.org/10.1038/nature11971. R. Kose and K. Kose, “An Accurate Dictionary Creation Method for MR Fingerprinting Using a Fast Bloch Simulator,” Magnetic Resonance in Medical Sciences 19, no. 3 (2020): 247–253, https://doi.org/10.2463/mrms.tn.2018‐0157. Y. Chen, Y. Jun, A. Heydari, et al., “MIMOSA: Multi‐Parametric Imaging Using Multiple‐Echoes With Optimized Simultaneous Acquisition for Highly‐Efficient Quantitative MRI,” Magnetic Resonance in Medicine 95, no. 3 (2026): 1528–1544, https://doi.org/10.1002/mrm.70143. M. Afzali, L. Mueller, K. Sakaie, et al., “MR Fingerprinting With b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan,” Magnetic Resonance in Medicine 88, no. 5 (2022): 2043–2057, https://doi.org/10.1002/mrm.29352. X. Cao, “DTI‐MR Fingerprinting for Rapid High‐Resolution Whole‐Brain T1, T2, Proton Density, ADC, and Fractional Anisotropy Mapping,” Magnetic Resonance in Medicine 91, no. 3 (2024): 987–1001, https://doi.org/10.1002/mrm.29916. K. J. Layton, S. Kroboth, F. Jia, et al., “Pulseq: A Rapid and Hardware‐Independent Pulse Sequence Prototyping Framework: Rapid Hardware‐Independent Pulse Sequence Prototyping,” Magnetic Resonance in Medicine 77, no. 4 (2017): 1544–1552, https://doi.org/10.1002/mrm.26235. K. Ravi, S. Geethanath, and J. Vaughan, “PyPulseq: A Python Package for MRI Pulse Sequence Design,” Journal of Open Source Software 4, no. 42 (2019): 1725, https://doi.org/10.21105/joss.01725. J. Endres, S. Weinmüller, H. N. Dang, and M. Zaiss, “Phase Distribution Graphs for Fast, Differentiable, and Spatially Encoded Bloch Simulations of Arbitrary MRI Sequences,” Magnetic Resonance in Medicine 92, no. 3 (2024): 1189–1204, https://doi.org/10.1002/mrm.30055. “BrainWeb: Simulated Brain Database,” McGill University, 2023, https://brainweb.bic.mni.mcgill.ca/brainweb/. D. Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging 13, no. 4 (2001): 534–546, https://doi.org/10.1002/jmri.1076. J. Z. Bojorquez, S. Bricq, C. Acquitter, F. Brunotte, P. M. Walker, and A. Lalande, “What Are Normal Relaxation Times of Tissues at 3 T?,” Magnetic Resonance Imaging 35 (2017): 69–80, https://doi.org/10.1016/j.mri.2016.08.021. A. M. Peters, M. J. Brookes, F. G. Hoogenraad, et al., “T2* Measurements in Human Brain at 1.5, 3 and 7 T,” Magnetic Resonance Imaging 25, no. 6 (2007): 748–753, https://doi.org/10.1016/j.mri.2007.02.014. T. Stöcker and S. Niesen, “Phase Graph Tutorial,” https://github.com/mrphysics‐bonn/epg‐tutorial. T. Stöcker, K. Vahedipour, D. Pflugfelder, and N. J. Shah, “High‐Performance Computing MRI Simulations,” Magnetic Resonance in Medicine 64, no. 1 (2010): 186–193, https://doi.org/10.1002/mrm.22406. F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier‐Hein, “nnU‐Net: A Self‐Configuring Method for Deep Learning‐Based Biomedical Image Segmentation,” Nature Methods 18, no. 2 (2021): 203–211, https://doi.org/10.1038/s41592‐020‐01008‐z. H. Zhong, C. M. Bianchi, S. J. Patel, A. R. Wolfe, and G. A. Visvikis, “Intracranial Migration of Intraocular Silicone Oil Following Repetitive Head Trauma,” Radiology Case Reports 14, no. 9 (2019): 1163–1166, https://doi.org/10.1016/j.radcr.2019.06.026. A. W. Eller, T. R. Friberg, and F. Mah, “Migration of Silicone Oil Into the Brain: A Complication of Intraocular Silicone Oil for Retinal Tamponade,” American Journal of Ophthalmology 129, no. 5 (2000): 685–688, https://doi.org/10.1016/S0002‐9394(00)00368‐8. M. B. Potts, A. C. Wu, D. J. Rusinak, K. Kesavabhotla, and B. S. Jahromi, “Seeing Floaters: A Case Report and Literature Review of Intraventricular Migration of Silicone Oil Tamponade Material for Retinal Detachment,” World Neurosurgery 115 (2018): 201–205, https://doi.org/10.1016/j.wneu.2018.04.049. |
| Grant Information: | d.hip Campus - Bavarian AIM; 500888779 Deutsche Forschungsgemeinschaft; 16SV9585 Bundesministerium für Bildung und Forschung; Competence Network for Scientific High Performance Computing in Bavaria (KONWIHR) |
| Contributed Indexing: | Keywords: RF‐spoiling; T1‐weighted FLASH; TR‐spoiling; diffusion; gradient‐spoiling; steady‐state |
| Substance Nomenclature: | 0 (Silicone Oils) |
| Entry Date(s): | Date Created: 20260622 Date Completed: 20260729 Latest Revision: 20260801 |
| Update Code: | 20260801 |
| PubMed Central ID: | PMC13421012 |
| DOI: | 10.1002/mrm.70443 |
| PMID: | 42324643 |
| Database: | MEDLINE |
| FullText | Links: – Type: other Url: https://resolver.ebsco.com:443/public/rma-ftfapi/ejs/direct?AccessToken=42DA9DCE915CD9D14005&Show=Object Text: Availability: 0 |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 42324643 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: T1-weighting in Steady-State FLASH MRI-Diffusion Is Not Only Supportive but Mandatory for the Contrast. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Weinmüller+S%22">Weinmüller S</searchLink>; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Chellapandian+DC%22">Chellapandian DC</searchLink>; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.; Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Endres+J%22">Endres J</searchLink>; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Leupold+J%22">Leupold J</searchLink>; Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.<br /><searchLink fieldCode="AU" term="%22Gritsch+F%22">Gritsch F</searchLink>; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Wagner+F%22">Wagner F</searchLink>; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Schneider+R%22">Schneider R</searchLink>; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.<br /><searchLink fieldCode="AU" term="%22Zaiss+M%22">Zaiss M</searchLink>; Institute of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany.; Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%228505245%22">Magnetic resonance in medicine</searchLink> [Magn Reson Med] 2026 Oct; Vol. 96 (4), pp. 1789-1797. <i>Date of Electronic Publication: </i>2026 Jun 21. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley%22">Wiley </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>8505245 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1522-2594 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2207403194%22">07403194 </searchLink><i>NLM ISO Abbreviation: </i>Magn Reson Med <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Publication</i>: 1999- : New York, NY : Wiley<br /><i>Original Publication</i>: San Diego : Academic Press – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Diffusion+Magnetic+Resonance+Imaging%22">Diffusion Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Diffusion+Magnetic+Resonance+Imaging+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging+methods%22">methods</searchLink><br /><searchLink fieldCode="MH" term="%22Silicone+Oils%22">Silicone Oils</searchLink>/<searchLink fieldCode="MH" term="%22Silicone+Oils+chemistry%22">chemistry</searchLink> ; <searchLink fieldCode="MH" term="%22Cerebrospinal+Fluid%22">Cerebrospinal Fluid</searchLink>/<searchLink fieldCode="MH" term="%22Cerebrospinal+Fluid+diagnostic+imaging%22">diagnostic imaging</searchLink> ; <searchLink fieldCode="MH" term="%22Phantoms%2C+Imaging%22">Phantoms, Imaging</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Algorithms%22">Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Diffusion%22">Diffusion</searchLink> ; <searchLink fieldCode="MH" term="%22Printing%2C+Three-Dimensional%22">Printing, Three-Dimensional</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: FLASH imaging is widely assumed to produce a T<subscript>1</subscript>-weighted steady-state contrast using RF- and gradient-spoiling. We observed substantial overestimation of CSF signals in simulations, when diffusion was neglected and realistic proton density was applied. This work investigates the role of diffusion in steady-state FLASH contrast formation and its implications for simulation-based modeling and measurement.<br />Methods: FLASH sequences were simulated using phase graph simulations using a synthetic brain phantom with and without diffusion and realistic PD values to show the contrast change. The impact of neglecting diffusion in synthetic training data was evaluated using a segmentation network trained on simulated data and tested on in vivo measurement. Experimental validation of the contrast change was performed using a 3D-printed brain phantom using silicone oil as a low-diffusivity compartment.<br />Results: Without diffusion, simulations showed CSF signal intensities higher than WM, resulting in a contrast change. Diffusion suppresses higher-order echoes in long T<subscript>2</subscript> tissues and is essential for achieving the T<subscript>1</subscript>-weighted steady-state contrast. A NN trained without diffusion fails to generalize to in vivo data and measurements with silicone oil compartments confirm the contrast changes in low-diffusivity media.<br />Conclusion: Diffusion is essential for realistic FLASH simulations of long T<subscript>2</subscript> tissues such as CSF. Steady-state FLASH contrast arises from the interplay of RF-, gradient-spoiling, and "multi-TR-relaxation-spoiling" governed by T<subscript>2</subscript>-decay and diffusion effects. For many quadratic phase cycling schemes, diffusion is required to obtain realistic T<subscript>1</subscript>-weighted contrast in MR simulations and should not be neglected in simulations or simulation-based deep learning applications.<br /> (© 2026 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.) – Name: Ref Label: References Group: RefInfo Data: M. Markl and J. Leupold, “Gradient Echo Imaging,” Journal of Magnetic Resonance Imaging 35, no. 6 (2012): 1274–1289, https://doi.org/10.1002/jmri.23638.<br />A. Haase, J. Frahm, D. Matthaei, W. Hanicke, and K.‐D. Merboldt, “FLASH Imaging. Rapid NMR Imaging Using Low Flip‐Angle Pulses,” Journal of Magnetic Resonance (1969) 67, no. 2 (1986): 258–266, https://doi.org/10.1016/0022‐2364(86)90433‐6.<br />A. P. Crawley, M. L. Wood, and R. M. Henkelman, “Elimination of Transverse Coherences in FLASH MRI,” Magnetic Resonance in Medicine 8, no. 3 (1988): 248–260, https://doi.org/10.1002/mrm.1910080303.<br />Y. Zur, M. L. Wood, and L. J. Neuringer, “Spoiling of Transverse Magnetization in Steady‐State Sequences,” Magnetic Resonance in Medicine 21, no. 2 (1991): 251–263, https://doi.org/10.1002/mrm.1910210210.<br />W. T. Sobol and D. M. Gauntt, “On the Stationary States in Gradient Echo Imaging,” Journal of Magnetic Resonance Imaging 6, no. 2 (1996): 384–398, https://doi.org/10.1002/jmri.1880060220.<br />J. Leupold, M. Weigel, and S. Bär, “On the Choice of the Phase Difference Increment in Radiofrequency‐Spoiled Gradient‐Echo Magnetic Resonance Imaging of Liquids With Consideration of Diffusion,” PLoS One 20, no. 5 (2025): e0324455, https://doi.org/10.1371/journal.pone.0324455.<br />C. Preibisch and R. Deichmann, “Influence of RF Spoiling on the Stability and Accuracy of T1 Mapping Based on Spoiled FLASH With Varying Flip Angles,” Magnetic Resonance in Medicine 61, no. 1 (2009): 125–135, https://doi.org/10.1002/mrm.21776.<br />V. Denolin, C. Azizieh, and T. Metens, “New Insights Into the Mechanisms of Signal Formation in RF‐Spoiled Gradient Echo Sequences,” Magnetic Resonance in Medicine 54, no. 4 (2005): 937–954, https://doi.org/10.1002/mrm.20652.<br />R. F. Busse and S. J. Riederer, “Steady‐State Preparation for Spoiled Gradient Echo Imaging,” Magnetic Resonance in Medicine 45, no. 4 (2001): 653–661, https://doi.org/10.1002/mrm.1088.<br />J. H. Duyn, “Steady State Effects in Fast Gradient Echo Magnetic Resonance Imaging,” Magnetic Resonance in Medicine 37, no. 4 (1997): 559–568, https://doi.org/10.1002/mrm.1910370414.<br />K. Nehrke, “On the Steady‐State Properties of Actual Flip Angle Imaging (AFI),” Magnetic Resonance in Medicine 61, no. 1 (2009): 84–92, https://doi.org/10.1002/mrm.21592.<br />N. Corbin and M. F. Callaghan, “Imperfect Spoiling in Variable Flip Angle T1 Mapping at 7T: Quantifying and Minimizing Impact,” Magnetic Resonance in Medicine 86, no. 2 (2021): 693–708, https://doi.org/10.1002/mrm.28720.<br />R. Treier, A. Steingoetter, M. Fried, W. Schwizer, and P. Boesiger, “Optimized and Combined T1 and B1 Mapping Technique for Fast and Accurate T1 Quantification in Contrast‐Enhanced Abdominal MRI,” Magnetic Resonance in Medicine 57, no. 3 (2007): 568–576, https://doi.org/10.1002/mrm.21177.<br />V. L. Yarnykh, “Optimal Radiofrequency and Gradient Spoiling for Improved Accuracy of T1 and B1 Measurements Using Fast Steady‐State Techniques,” Magnetic Resonance in Medicine 63, no. 6 (2010): 1610–1626, https://doi.org/10.1002/mrm.22394.<br />F. H. Epstein, J. P. Mugler, and J. R. Brookeman, “Spoiling of Transverse Magnetization in Gradient‐Echo (GRE) Imaging During the Approach to Steady State,” Magnetic Resonance in Medicine 35, no. 2 (1996): 237–245, https://doi.org/10.1002/mrm.1910350216.<br />S. Weinmüller, J. Endres, H. N. Dang, R. Stollberger, and M. Zaiss, “MR‐Zero Meets FLASH—Controlling the Transient Signal Decay in Gradient‐ and Rf‐Spoiled Gradient Echo Sequences,” Magnetic Resonance in Medicine 93 (2024): 942–960, https://doi.org/10.1002/mrm.30318.<br />C. Ganter, “Steady State of Echo–Shifted Sequences With Radiofrequency Phase Cycling,” Magnetic Resonance in Medicine 56, no. 4 (2006): 923–926, https://doi.org/10.1002/mrm.21010.<br />R. Heule, C. Ganter, and O. Bieri, “Variable Flip Angle T1 Mapping in the Human Brain With Reduced T2 Sensitivity Using Fast Radiofrequency‐Spoiled Gradient Echo Imaging,” Magnetic Resonance in Medicine 75, no. 4 (2016): 1413–1422, https://doi.org/10.1002/mrm.25668.<br />K. E. Keenan, M. Ainslie, A. J. Barker, et al., “Quantitative Magnetic Resonance Imaging Phantoms: A Review and the Need for a System Phantom,” Magnetic Resonance in Medicine 79, no. 1 (2018): 48–61, https://doi.org/10.1002/mrm.26982.<br />M. E. Komlosh, D. Benjamini, A. S. Barnett, et al., “Anisotropic Phantom to Calibrate High‐q Diffusion MRI Methods,” Journal of Magnetic Resonance 275 (2017): 19–28, https://doi.org/10.1016/j.jmr.2016.11.017.<br />C. Poupon, B. Rieul, I. Kezele, M. Perrin, F. Poupon, and J. Mangin, “New Diffusion Phantoms Dedicated to the Study and Validation of High‐Angular‐Resolution Diffusion Imaging (HARDI) Models,” Magnetic Resonance in Medicine 60, no. 6 (2008): 1276–1283, https://doi.org/10.1002/mrm.21789.<br />H. M. Gach, “Technical Note: T1 and T2 and Complex Permittivities of Mineral Oil, Silicone Oil, and Glycerol at 0.35, 1.5, and 3 T,” Medical Physics 46, no. 4 (2019): 1785–1792, https://doi.org/10.1002/mp.13429.<br />B. Webb, “Temperature Dependence of Viscosity, Relaxation Times (T1, T2) and Simulated Contrast for Potential Perfusates in Post‐Mortem MR Angiography (PMMRA),” International Journal of Legal Medicine 131, no. 3 (2017): 739–749, https://doi.org/10.1007/s00414‐016‐1482‐5.<br />N. Scholand, X. Wang, V. Roeloffs, S. Rosenzweig, and M. Uecker, “Quantitative MRI by Nonlinear Inversion of the Bloch Equations,” Magnetic Resonance in Medicine 90, no. 2 (2023): 520–538, https://doi.org/10.1002/mrm.29664.<br />J. Fessler, “Model‐Based Image Reconstruction for MRI,” IEEE Signal Processing Magazine 27, no. 4 (2010): 81–89, https://doi.org/10.1109/MSP.2010.936726.<br />A. S. Lundervold and A. Lundervold, “An Overview of Deep Learning in Medical Imaging Focusing on MRI,” Zeitschrift für Medizinische Physik 29, no. 2 (2019): 102–127, https://doi.org/10.1016/j.zemedi.2018.11.002.<br />C. G. Xanthis, D. Filos, K. Haris, and A. H. Aletras, “Simulator‐Generated Training Datasets as an Alternative to Using Patient Data for Machine Learning: An Example in Myocardial Segmentation With MRI,” Computer Methods and Programs in Biomedicine 198 (2021): 105817, https://doi.org/10.1016/j.cmpb.2020.105817.<br />Y. Chen, Z. Fang, S.‐C. Hung, W.‐T. Chang, D. Shen, and W. Lin, “High‐Resolution 3D MR Fingerprinting Using Parallel Imaging and Deep Learning,” NeuroImage 206 (2020): 116329, https://doi.org/10.1016/j.neuroimage.2019.116329.<br />D. Ma, V. Gulani, N. Seiberlich, et al., “Magnetic Resonance Fingerprinting,” Nature 495, no. 7440 (2013): 187–192, https://doi.org/10.1038/nature11971.<br />R. Kose and K. Kose, “An Accurate Dictionary Creation Method for MR Fingerprinting Using a Fast Bloch Simulator,” Magnetic Resonance in Medical Sciences 19, no. 3 (2020): 247–253, https://doi.org/10.2463/mrms.tn.2018‐0157.<br />Y. Chen, Y. Jun, A. Heydari, et al., “MIMOSA: Multi‐Parametric Imaging Using Multiple‐Echoes With Optimized Simultaneous Acquisition for Highly‐Efficient Quantitative MRI,” Magnetic Resonance in Medicine 95, no. 3 (2026): 1528–1544, https://doi.org/10.1002/mrm.70143.<br />M. Afzali, L. Mueller, K. Sakaie, et al., “MR Fingerprinting With b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan,” Magnetic Resonance in Medicine 88, no. 5 (2022): 2043–2057, https://doi.org/10.1002/mrm.29352.<br />X. Cao, “DTI‐MR Fingerprinting for Rapid High‐Resolution Whole‐Brain T1, T2, Proton Density, ADC, and Fractional Anisotropy Mapping,” Magnetic Resonance in Medicine 91, no. 3 (2024): 987–1001, https://doi.org/10.1002/mrm.29916.<br />K. J. Layton, S. Kroboth, F. Jia, et al., “Pulseq: A Rapid and Hardware‐Independent Pulse Sequence Prototyping Framework: Rapid Hardware‐Independent Pulse Sequence Prototyping,” Magnetic Resonance in Medicine 77, no. 4 (2017): 1544–1552, https://doi.org/10.1002/mrm.26235.<br />K. Ravi, S. Geethanath, and J. Vaughan, “PyPulseq: A Python Package for MRI Pulse Sequence Design,” Journal of Open Source Software 4, no. 42 (2019): 1725, https://doi.org/10.21105/joss.01725.<br />J. Endres, S. Weinmüller, H. N. Dang, and M. Zaiss, “Phase Distribution Graphs for Fast, Differentiable, and Spatially Encoded Bloch Simulations of Arbitrary MRI Sequences,” Magnetic Resonance in Medicine 92, no. 3 (2024): 1189–1204, https://doi.org/10.1002/mrm.30055.<br />“BrainWeb: Simulated Brain Database,” McGill University, 2023, https://brainweb.bic.mni.mcgill.ca/brainweb/.<br />D. Le Bihan, “Diffusion Tensor Imaging: Concepts and Applications,” Journal of Magnetic Resonance Imaging 13, no. 4 (2001): 534–546, https://doi.org/10.1002/jmri.1076.<br />J. Z. Bojorquez, S. Bricq, C. Acquitter, F. Brunotte, P. M. Walker, and A. Lalande, “What Are Normal Relaxation Times of Tissues at 3 T?,” Magnetic Resonance Imaging 35 (2017): 69–80, https://doi.org/10.1016/j.mri.2016.08.021.<br />A. M. Peters, M. J. Brookes, F. G. Hoogenraad, et al., “T2* Measurements in Human Brain at 1.5, 3 and 7 T,” Magnetic Resonance Imaging 25, no. 6 (2007): 748–753, https://doi.org/10.1016/j.mri.2007.02.014.<br />T. Stöcker and S. Niesen, “Phase Graph Tutorial,” https://github.com/mrphysics‐bonn/epg‐tutorial.<br />T. Stöcker, K. Vahedipour, D. Pflugfelder, and N. J. Shah, “High‐Performance Computing MRI Simulations,” Magnetic Resonance in Medicine 64, no. 1 (2010): 186–193, https://doi.org/10.1002/mrm.22406.<br />F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier‐Hein, “nnU‐Net: A Self‐Configuring Method for Deep Learning‐Based Biomedical Image Segmentation,” Nature Methods 18, no. 2 (2021): 203–211, https://doi.org/10.1038/s41592‐020‐01008‐z.<br />H. Zhong, C. M. Bianchi, S. J. Patel, A. R. Wolfe, and G. A. Visvikis, “Intracranial Migration of Intraocular Silicone Oil Following Repetitive Head Trauma,” Radiology Case Reports 14, no. 9 (2019): 1163–1166, https://doi.org/10.1016/j.radcr.2019.06.026.<br />A. W. Eller, T. R. Friberg, and F. Mah, “Migration of Silicone Oil Into the Brain: A Complication of Intraocular Silicone Oil for Retinal Tamponade,” American Journal of Ophthalmology 129, no. 5 (2000): 685–688, https://doi.org/10.1016/S0002‐9394(00)00368‐8.<br />M. B. Potts, A. C. Wu, D. J. Rusinak, K. Kesavabhotla, and B. S. Jahromi, “Seeing Floaters: A Case Report and Literature Review of Intraventricular Migration of Silicone Oil Tamponade Material for Retinal Detachment,” World Neurosurgery 115 (2018): 201–205, https://doi.org/10.1016/j.wneu.2018.04.049. – Name: GrantInfo Label: Grant Information Group: Grant Data: d.hip Campus - Bavarian AIM; 500888779 Deutsche Forschungsgemeinschaft; 16SV9585 Bundesministerium für Bildung und Forschung; Competence Network for Scientific High Performance Computing in Bavaria (KONWIHR) – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>RF‐spoiling; T1‐weighted FLASH; TR‐spoiling; diffusion; gradient‐spoiling; steady‐state – Name: NumberCAS Label: Substance Nomenclature Group: ID Data: 0 (Silicone Oils) – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260622 <i>Date Completed: </i>20260729 <i>Latest Revision: </i>20260801 – Name: DateUpdate Label: Update Code Group: Date Data: 20260801 – Name: PubmedCentralID Label: PubMed Central ID Group: ID Data: PMC13421012 – Name: DOI Label: DOI Group: ID Data: 10.1002/mrm.70443 – Name: AN Label: PMID Group: ID Data: 42324643 |
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